Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity-Reference-Cited by-同舟云学术

Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity

Published:2023-03 Issue:3 Volume:8 Page:2244-2256
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Liu Jierui¹^ORCID,Cao Zhiqiang¹^ORCID,Liu Xilong¹^ORCID,Wang Shuo¹^ORCID,Yu Junzhi²^ORCID

Affiliation:

1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. State Key Laboratory for Turbulence and Complex System, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/10109992/09904879.pdf?arnumber=9904879

Reference58 articles.

1. Self-Supervised Monocular Depth Hints

2. Unsupervised monocular depth learning in dynamic scenes;li;Proc Conf Robot Learn,2020

3. Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

4. Self-Supervised Learning With Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera

5. GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

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